cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Husni Teja Sukmana
Contact Email
husni@bright-journal.org
Phone
+62895422720524
Journal Mail Official
jads@bright-journal.org
Editorial Address
Gedung FST UIN Jakarta, Jl. Lkr. Kampus UIN, Cemp. Putih, Kec. Ciputat Tim., Kota Tangerang Selatan, Banten 15412
Location
Kota adm. jakarta pusat,
Dki jakarta
INDONESIA
Journal of Applied Data Sciences
Published by Bright Publisher
ISSN : -     EISSN : 27236471     DOI : doi.org/10.47738/jads
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management
One of the current hot topics in science is data: how can datasets be used in scientific and scholarly research in a more reliable, citable and accountable way? Data is of paramount importance to scientific progress, yet most research data remains private. Enhancing the transparency of the processes applied to collect, treat and analyze data will help to render scientific research results reproducible and thus more accountable. The datasets itself should also be accessible to other researchers, so that research publications, dataset descriptions, and the actual datasets can be linked. The journal Data provides a forum to publish methodical papers on processes applied to data collection, treatment and analysis, as well as for data descriptors publishing descriptions of a linked dataset.
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 477 Documents
 6   7   8   9   10 
Ensemble learning techniques to improve the accuracy of predictive model performance in the scholarship selection process Buslim, Nurhayati; Zulfiandri, Zulfiandri; KyungOh, Lee
Journal of Applied Data Sciences Vol 4, No 3: SEPTEMBER 2023
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v4i3.112

Abstract

Ensemble Learning is an algorithm that searches for the best prediction result based on several classifier solutions which are come from different algorithms. Ensemble learning has better accuracy and performance compared to other algorithms because this method uses several learning algorithms to achieve better predictive solutions. There are a lot of data that the scholarship organizer receives and manages. This makes the process take a lot of time to do checking process and makes it inefficient. Accelerating the process while also maintaining the accuracy of the scholarship selection process can certainly improve the selection performance. In this study, we process student data from UIN Jakarta University as a sample. The model will have 2 base classifiers, namely Support Vector Machine (SVM) and Key Nearest Neighbor (KNN). Each of these algorithms already has quite a good accuracy. Using Ensemble Learning improves the model performance because it has the ability to overcome errors that occur in each data prediction. We can exploit the classification application created using "Streamlit" and will determine whether a student is accepted or rejected in the scholarship selection process. Our model and application can be used by academics as a Decision Support System (DSS) for determining scholarship recipients. This model can also be used as a development for institutions in the academic field.
A Comparative Study of Feature Selection Techniques in Machine Learning for Predicting Stock Market Trends Paramita, Adi Suryaputra; Winata, Shalomeira Valencia
Journal of Applied Data Sciences Vol 4, No 3: SEPTEMBER 2023
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v4i3.99

Abstract

This study aims to compare the effectiveness of three feature selection techniques, namely Principal Component Analysis (PCA), Information Gain (IG), and Recursive Feature Elimination (RFE), in predicting stock market conditions. This research uses three distinct Kaggle datasets that contain data for predicting stock market values. The results show that RFE performs better than PCA and IG in predicting market value with fairly precise accuracy. By using the RFE technique, this study was able to identify the most influential features in prediction, reduce the dimensionality of the data, and improve the performance of the prediction model. These provide significant benefits in the world of stocks, including improved investment decisions, reduced investment risk, improved trading strategy performance, and identification of promising investment opportunities. For future research, further comparative studies between other feature selection techniques can be conducted. This research has novelty in several aspects. First, it applies different feature selection techniques, namely Principal Component Analysis (PCA), Information Gain (IG), and Recursive Feature Elimination (RFE), in the context of stock market prediction. Utilizing these techniques to select the most relevant features in predicting stock market conditions provides a deeper understanding of the influence of these features on stock price movements. Furthermore, this research utilizes different datasets from Kaggle, which represent various stock market value predictions. The utilization of diverse datasets provides variation in the data and allows this research to examine the performance of feature selection techniques in multiple stock market contexts. In conclusion, this research provides insight into the effectiveness of feature selection techniques in stock market value prediction. It also provides actionable guidance for market participants to improve investment decisions and trading performance in the stock market.
Assessing Factors and Simulating Innovation: A Study of Innovative Capacities Among Data Science Professionals in China Zhang, Yongfeng; Sangsawang, Thosporn; Vipahasna, Piyanan Pannim
Journal of Applied Data Sciences Vol 4, No 3: SEPTEMBER 2023
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v4i3.123

Abstract

This study aims to analyze the multifaceted factors influencing the innovative capabilities of data science professionals in China and assess the impact of simulations on their innovative skills. The sample comprises seventeen experts who actively participated in discussions and provided 36 perspectives on the factors affecting their innovation abilities. The research methodology utilized the Delphi method, involving four rounds of questionnaires distributed to 363 data science professionals to evaluate the factors affecting their innovation capacity. The data was rigorously analyzed using mathematical statistics and SPSS, with a strong emphasis on questionnaire validity and reliability. In the reliability analysis, Cronbach's α was found to be 0.98, indicating a high level of internal consistency. The research results yielded an average score of 4.79, SD = 0.39, IQR = 1, reflecting a strong consensus among experts in agreement with the research findings. Exploratory factor analysis was employed for validity assessment, revealing that the 12th factor accounted for a cumulative variance explanation rate of 76.54%, exceeding the threshold of 60%, signifying the robust structural validity of the questionnaire data. The study also utilized AMOS software to simulate sample data and assess the influence coefficients of individual, organizational, and family characteristics on innovation capacity, resulting in values of 0.53, 0.39, and 0.22, respectively, all greater than 0, indicating favorable influence relationships. Building upon these findings, a comprehensive model of creativity abilities among Chinese data science professionals is proposed. This research critically examines the innovation potential of data science professionals in Chinese academia, with the overarching goal of enhancing their creative skills and competitiveness within the data science field. Additionally, it lays the theoretical groundwork for fostering innovation within the university setting.
Modelling Data Warehousing and Business Intelligence Architecture for Non-profit Organization Based on Data Governances Framework Paramita, Adi Suryaputra; Prabowo, Harjanto; Ramadhan, Arief; Sensuse, Dana Indra
Journal of Applied Data Sciences Vol 4, No 3: SEPTEMBER 2023
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v4i3.117

Abstract

Information systems research for non-profit organizations is an opportunity to make a contribution to the field of information systems, the adoption of information systems in this field is relatively tedious and there are few studies that examine this area; consequently, there are several research gaps in the domain of non-profit organizations that need to be solved. This research will focus on the development of data warehouse architecture and business intelligence for non-profit organizations. In this study, the Soft Systems Methodology (SSM) technique will be employed to develop a data warehouse architecture and business intelligence. This research will interview twenty individuals to collect primary data, review organizational policy documents, and conduct an open-ended survey. The obtained data will then be qualitatively analyzed, resulting in the formation of rich picture diagrams, CATWOE analysis, and conceptual models, which will ultimately form a data warehouse architecture and business intelligence. This research has produced a microservices-enhanced data warehouse architecture and business intelligence for non-profit organizations.
Mean-Median Smoothing Backpropagation Neural Network to Forecast Unique Visitors Time Series of Electronic Journal Wibawa, Aji Prasetya; Utama, Agung Bella Putra; Lestari, Widya; Saputra, Irzan Tri; Izdihar, Zahra Nabila; Pujianto, Utomo; Haviluddin, Haviluddin; Nafalski, Andrew
Journal of Applied Data Sciences Vol 4, No 3: SEPTEMBER 2023
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v4i3.97

Abstract

Sessions or unique visitors is the number of visitors from one IP who accessed a journal portal for the first time in a certain period of time. The large number of unique daily average subscriber visits to electronic journal pages indicates that this scientific periodical is in high demand. Hence, the number of unique visitors is an important indicator of the accomplishment of an electronic journal as a measure of the dissemination in accelerating the journal accreditation system. Numerous methods can be used for forecasting, one of which is the backpropagation neural network (BPNN). Data quality is very important in building a good BPNN model, because the success of modeling at BPNN is very dependent on input data. One way that can be carried out to improve data quality is by smoothing the data. In this study, the forecasting method for predicting time series data for unique visitors to electronic journals employed three models, respectively BPNN, BPNN with mean smoothing, and BPNN with median smoothing. Based on the findings, the results of the smallest error were obtained by the BPNN model with a mean smoothing with MSE 0.00129 and RMSE 0.03518 with a learning rate of 0.4 on 1-2-1 architecture which can be used as a forecast for unique visitors of electronic journals.
Sentiment Unleashed: Electric Vehicle Incentives Under the Lens of Support Vector Machine and TF-IDF Analysis Batmetan, Johan Reimon; Hariguna, Taqwa
Journal of Applied Data Sciences Vol 5, No 1: JANUARY 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i1.162

Abstract

This research examines public sentiment regarding electric vehicle incentives through sentiment analysis of online comments. These incentives include tax deductions and other financial rewards offered to promote the adoption of electric vehicles. In this study, the researchers collected and analyzed over 1,000 comments from various online platforms to understand the public's perspective on these incentives. The study employs Support Vector Machine (SVM), a powerful machine learning algorithm, as the main method and utilizes Term Frequency-Inverse Document Frequency (TF-IDF) to analyze comment texts. The research findings depict significant variation in public sentiment regarding electric vehicle incentives. Approximately 57.3% of comments express negative sentiment towards these incentives, while 33.2% are positive, and the rest are neutral. There is strong support for these incentives, particularly from a financial standpoint. However, some dissatisfaction is expressed, especially regarding electric vehicle prices and charging infrastructure availability. External factors such as government policies and vehicle prices significantly influence public sentiment. Easy access to charging infrastructure also plays a crucial role in shaping positive sentiment. Environmental issues also contribute to a positive view of electric vehicle incentives. Policy recommendations arising from this research emphasize the need to consider these factors when designing and implementing electric vehicle incentives. Improvement efforts in pricing, infrastructure, and environmental education can help enhance electric vehicle adoption in society. This research provides valuable insights into public sentiment towards electric vehicle incentives and the factors influencing such sentiment. The results can serve as a foundation for better decision-making to support the development of sustainable and environmentally friendly electric vehicles.   
A Comparative Study on Data Collection Methods: Investigating Optimal Datasets for Data Mining Analysis Jatnika, Hendra; Waluyo, Ari; Azis, Abdul
Journal of Applied Data Sciences Vol 5, No 1: JANUARY 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i1.148

Abstract

This study is dedicated to evaluating the efficiency of diverse data collection methods in obtaining optimal data for computational data mining. The investigation meticulously compares the questionnaire and web mining methodologies within the framework of SVM and NBC algorithms to discern the flexibility inherent in each data type. The outcomes of this comprehensive analysis demonstrate that questionnaires showcase remarkable flexibility, exhibiting accuracy rates surpassing 80% in both algorithms, along with AUC values exceeding 0.9 when contrasted with data acquired through web mining techniques. These results underscore the paramount importance of the dataset collection method in the realm of computational data mining. The study contributes compelling evidence that advocates for the superiority of the questionnaire data collection method over web mining in the specific context of computational data mining. The questionnaire method not only outperforms in terms of flexibility but also achieves high accuracy, making it a more reliable choice for acquiring data in this domain. Beyond its practical implications, the research highlights a critical aspect of methodology in data collection by emphasizing the necessity of exploring and assessing methods that may have been overlooked in previous research endeavors. This underscores the continuous evolution of research methodologies and the need for ongoing exploration to enhance the robustness and effectiveness of data collection in computational data mining studies.   
Image Classifier based on Histogram Matching and Outlier Detection using Hellinger distance Gupta, Anamika; Kochchar, Sarabjeet Kaur; Joshi, Anurag
Journal of Applied Data Sciences Vol 4, No 4: DECEMBER 2023
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v4i4.114

Abstract

In this paper, we developed a prediction model based on histogram matching of Chest X-ray images. Hellinger distance metric is used to match two histograms. The chest x-ray images are pre-processed and converted to histograms. A benchmark histogram is obtained by finding the average of all pixel intensity values. Then outlier images are detected by comparing the histogram of an image with the benchmark histogram using the hellinger metric. Finally, a prediction method is proposed which matches the histogram of unseen images to histograms of nearest neighbor images.  Hypertuning of input parameters to the proposed prediction method is performed to get the best set of parameters. The proposed model gives an accuracy of 92.3 % and F1 score of 94.6 % on the training set, accuracy of 86.2% and F1 score of 89.6% on the test set.
Exploring Essential Skills for Sustainable Community Leadership: A Data Analysis Perspective on SDGs Alignment and Model Pigultong, Matee
Journal of Applied Data Sciences Vol 5, No 1: JANUARY 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i1.174

Abstract

This research explores the essential skills required by Thai Buddhist moral teacher monks as community leaders for sustainable development. These monks, representing Buddhism, play a crucial role in conserving teachings and fostering spiritual well-being. The study, employing mixed methods, identifies a comprehensive set of 17 skills necessary for their leadership roles, ranging from academic coordination to digital media- based Dhamma teaching and environmental management. The findings highlight a direct alignment between these skills and specific United Nations' Sustainable Development Goals (SDGs). The identified skills contribute significantly to achieving inclusive education, sustainable economic growth, resilient infrastructure, responsible consumption, environmental protection, and the promotion of peaceful societies. In conclusion, this research emphasizes the importance of cultivating a diverse skill set among Thai Buddhist moral teacher monks, offering valuable insights for policymakers, educators, and religious institutions seeking to enhance leadership capabilities and align efforts with global sustainability objectives.
Exploring ADR Trends: A Data Mining Approach to Hotel Room Pricing, Cancellations, and EDA Hikmawati, Nina Kurnia; Ramdhani, Yudi; Wartika, Wartika
Journal of Applied Data Sciences Vol 5, No 1: JANUARY 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i1.165

Abstract

This study investigates the intricacies of hotel reservation cancellations by analyzing a comprehensive dataset that includes information from both City Hotel and Resort Hotel. Through a thorough examination of various aspects, the research provides detailed insights into cancellation tendencies, daily rates, seasonal trends, and the influence of geographic factors and market segments on cancellation behavior. The overall cancellation and non-cancellation ratios indicate a notable non-cancellation rate of 62.86%, showcasing a high level of guest confidence in their reservations. Conversely, the 37.14% cancellation ratio raises concerns about potential negative repercussions. A comparative analysis between City Hotel and Resort Hotel reveals a significant difference in cancellation rates, emphasizing the need for tailored strategies at City Hotel to enhance booking stability. The study on Average Daily Rate (ADR) for both hotels bring attention to price differences and seasonal trends. Resort Hotel's higher ADR suggests potential advantages in location or amenities. Seasonal trends, particularly the highest ADR during the summer, provide valuable insights for resource planning. The variation in cancellation rates based on countries emphasizes the importance of focused strategies in regions with high cancellation rates, as seen with Portugal having the highest cancellation rate (77.70%). Analysis of hotel customer market segments identifies Online Travel Agencies (OTA) as the segment with the highest cancellation rate (46.97%). These findings present opportunities for tailored marketing and cancellation policies based on the characteristics of each segment. In conclusion, this research offers strategic insights for hotel managers to enhance booking stability, design competitive pricing policies, and understand the impact of geographic factors and market segments on cancellation behavior.

Page 8 of 48 | Total Record : 477

 6   7   8   9   10 

Filter by Year

2020 2025


Reset
Filter By Issues
All Issue Vol 6, No 4: December 2025 Vol 6, No 3: September 2025 Vol 6, No 2: MAY 2025 Vol 6, No 1: JANUARY 2025 Vol 5, No 4: DECEMBER 2024 Vol 5, No 3: SEPTEMBER 2024 Vol 5, No 2: MAY 2024 Vol 5, No 1: JANUARY 2024 Vol 4, No 4: DECEMBER 2023 Vol 4, No 3: SEPTEMBER 2023 Vol 4, No 2: MAY 2023 Vol 4, No 1: JANUARY 2023 Vol 3, No 4: DECEMBER 2022 Vol 3, No 3: SEPTEMBER 2022 Vol 3, No 2: MAY 2022 Vol 3, No 1: JANUARY 2022 Vol 2, No 4: DECEMBER 2021 Vol 2, No 3: SEPTEMBER 2021 Vol 2, No 2: MAY 2021 Vol 2, No 1: JANUARY 2021 Vol 1, No 2: DECEMBER 2020 Vol 1, No 1: SEPTEMBER 2020 More Issue